{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.stats import skew, kurtosis\n",
    "from scipy.signal import butter, filtfilt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ECG(df): \n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    # plt.plot(df[\"EcgWaveform\"][sample_range[0]:sample_range[1]], color='blue', label='ECG Waveform (df_s)')\n",
    "    plt.plot(df[\"EcgWaveform\"], color='blue', label='ECG Waveform (df_s)')\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "    plt.title(\"ECG Waveform from df_s\")\n",
    "    plt.grid(alpha=0.3)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.signal import butter, filtfilt\n",
    "\n",
    "def preprocessing_and_save(df, output_path):\n",
    "    # 创建输出文件夹\n",
    "    output_dir = os.path.dirname(output_path)\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "    \n",
    "    # 采样率\n",
    "    fs = 250  \n",
    "    lowcut = 0.5  # 带通滤波器的低频截止频率\n",
    "    highcut = 40  # 带通滤波器的高频截止频率\n",
    "\n",
    "    # 转换ECG信号为毫伏\n",
    "    ecg_signal = (df['EcgWaveform'].values.astype(float) - 1024) / 200\n",
    "\n",
    "    # 应用带通滤波器\n",
    "    nyquist_rate = fs / 2.0\n",
    "    low = lowcut / nyquist_rate\n",
    "    high = highcut / nyquist_rate\n",
    "    b, a = butter(2, [low, high], btype='band')\n",
    "    ecg_cleaned = filtfilt(b, a, ecg_signal)\n",
    "\n",
    "    # 创建新的DataFrame保存Time和处理后的ECG信号\n",
    "    new_df = pd.DataFrame({\n",
    "        'Time': df['Time'],        # 原始Time列\n",
    "        'EcgWaveform': ecg_cleaned  # 处理后的ECG信号\n",
    "    })\n",
    "\n",
    "    # 保存为CSV文件\n",
    "    new_df.to_csv(output_path, index=False)\n",
    "    print(f\"文件已保存到 {output_path}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文件已保存到 ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data/2014_10_02-06_14_52/2014_10_02-06_14_52_ECG.csv\n",
      "文件已保存到 ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data/2014_10_03-08_21_59/2014_10_03-08_21_59_ECG.csv\n",
      "文件已保存到 ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data/2014_10_04-15_03_37/2014_10_04-15_03_37_ECG.csv\n",
      "文件已保存到 ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data/2014_10_01-05_59_30/2014_10_01-05_59_30_ECG.csv\n",
      "文件已保存到 ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data/2014_10_04-09_09_29/2014_10_04-09_09_29_ECG.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "folder_path = \"d1namo-ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data\"  \n",
    "output_path = \"ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/009/sensor_data\" \n",
    "\n",
    "for dirname, _, filenames in os.walk(folder_path):\n",
    "    \n",
    "    for filename in filenames:\n",
    "        if filename == '.DS_Store' or filename == '--waitMarkerFilePath':\n",
    "            continue\n",
    "        else:\n",
    "            # print(filename)\n",
    "            df = pd.read_csv(folder_path + '/' + filename[0:-8] + '/' + filename)\n",
    "            preprocessing_and_save(df,output_path + '/' + filename[0:-8] + '/' + filename)\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def preprocessing_glucose(df, output_path):\n",
    "    # 创建输出文件夹\n",
    "    output_dir = os.path.dirname(output_path)\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "    \n",
    "    glucose_mg=[]\n",
    "    \n",
    "    for i in range(len(df['glucose'])):\n",
    "        glucose_mg.append(float(df['glucose'][i])*18)\n",
    "    \n",
    "    \n",
    "    # glucose_mg = df['glucose']*18   # 1mmol/L=18mg/dL\n",
    "\n",
    "\n",
    "    new_df = pd.DataFrame({\n",
    "        'date': df['date'],   \n",
    "        'time': df['time'],      \n",
    "        'glucose': glucose_mg\n",
    "    })\n",
    "\n",
    "    # 保存为CSV文件\n",
    "    new_df.to_csv(output_path, index=False)\n",
    "    print(f\"文件已保存到 {output_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d1namo-ecg-glucose-data/healthy_subset_pictures-glucose-food/healthy_subset_pictures-glucose-food/017\n",
      "food.csv\n",
      "d1namo-ecg-glucose-data/healthy_subset_pictures-glucose-food/healthy_subset_pictures-glucose-food/017\n",
      "food_pictures\n",
      "d1namo-ecg-glucose-data/healthy_subset_pictures-glucose-food/healthy_subset_pictures-glucose-food/017\n",
      "annotations.csv\n",
      "文件已保存到 ecg-glucose-data/healthy_subset_glucose/017/glucose.csv\n"
     ]
    }
   ],
   "source": [
    "folder_path = \"d1namo-ecg-glucose-data/healthy_subset_pictures-glucose-food/healthy_subset_pictures-glucose-food/017\"  \n",
    "output_path = \"ecg-glucose-data/healthy_subset_glucose/017\" \n",
    "\n",
    "for file_name in os.listdir(folder_path):\n",
    "\n",
    "    if file_name == 'glucose.csv':\n",
    "        df = pd.read_csv(folder_path + '/' + file_name)\n",
    "        preprocessing_glucose(df,output_path + '/' + file_name)\n",
    "    else:\n",
    "        print(folder_path)\n",
    "        print(file_name)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "folder_path = \"ecg-glucose-data/diabetes_subset_ecg_data/diabetes_subset_ecg_data/007/sensor_data\" \n",
    "\n",
    "for dirname, _, filenames in os.walk(folder_path):\n",
    "    \n",
    "    for filename in filenames:\n",
    "        df = pd.read_csv(folder_path + '/' + filename[0:-8] + '/' + filename)\n",
    "        plot_ECG(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有 CSV 文件已合并，保存到 ecg-glucose-data/diabetes_subset_ecg_data/009.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 设置主文件夹路径和保存路径\n",
    "folder_path = \"ecg-glucose-data/diabetes_subset_ecg_data/009/sensor_data\"\n",
    "output_file = \"ecg-glucose-data/diabetes_subset_ecg_data/009.csv\"\n",
    "\n",
    "# 获取所有 CSV 文件路径（包括子文件夹）\n",
    "csv_files = []\n",
    "for root, _, files in os.walk(folder_path):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "            csv_files.append(os.path.join(root, file))  # 获取完整路径\n",
    "\n",
    "# 读取并合并 CSV 文件\n",
    "df_list = [pd.read_csv(file) for file in csv_files]  # 读取所有 CSV\n",
    "merged_df = pd.concat(df_list, ignore_index=True)  # 合并数据\n",
    "\n",
    "# 保存到指定路径\n",
    "merged_df.to_csv(output_file, index=False)\n",
    "\n",
    "print(f\"所有 CSV 文件已合并，保存到 {output_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据划分完成，已保存到 hypo.csv, normal.csv, hyper.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 读取 ECG 数据\n",
    "ecg_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_ecg_data/020.csv\")\n",
    "ecg_df[\"Time\"] = pd.to_datetime(ecg_df[\"Time\"], format=\"%d/%m/%Y %H:%M:%S.%f\", errors='coerce')  # 解析时间\n",
    "\n",
    "# 读取血糖数据\n",
    "glucose_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_glucose/020/glucose.csv\")\n",
    "glucose_df[\"datetime\"] = pd.to_datetime(glucose_df[\"date\"] + \" \" + glucose_df[\"time\"], format=\"%Y-%m-%d %H:%M:%S\", errors='coerce')\n",
    "\n",
    "# 获取 ECG 记录的最早和最晚时间\n",
    "ecg_start_time = ecg_df[\"Time\"].min()\n",
    "ecg_end_time = ecg_df[\"Time\"].max()\n",
    "\n",
    "# 目标 ECG 片段长度\n",
    "target_length = 5 * 60 * 250  # 5分钟，每秒250个采样点\n",
    "\n",
    "# 创建存储列表\n",
    "hypo_list = []\n",
    "normal_list = []\n",
    "hyper_list = []\n",
    "\n",
    "# 遍历血糖测量点\n",
    "for _, row in glucose_df.iterrows():\n",
    "    measure_time = row[\"datetime\"]  # 当前血糖测量时间\n",
    "    glucose_value = row[\"glucose\"]  # 当前血糖值\n",
    "\n",
    "    # **优化1：如果血糖测量时间超出 ECG 数据范围，直接跳过**\n",
    "    if measure_time < ecg_start_time:\n",
    "        continue  # 跳过早于 ECG 记录的时间点\n",
    "    if measure_time > ecg_end_time:\n",
    "        break  # **超出 ECG 记录的最晚时间，停止遍历**\n",
    "\n",
    "    # 选取前 5 分钟 ECG 片段\n",
    "    start_time = measure_time - pd.Timedelta(minutes=5)\n",
    "    ecg_segment = ecg_df[(ecg_df[\"Time\"] >= start_time) & (ecg_df[\"Time\"] < measure_time)]\n",
    "\n",
    "    # **优化2：如果 ECG 片段为空，跳过该测量点**\n",
    "    if ecg_segment.empty:\n",
    "        continue\n",
    "\n",
    "    # 仅保留 EcgWaveform 数据\n",
    "    ecg_values = ecg_segment[\"EcgWaveform\"].values.flatten()\n",
    "\n",
    "    # 确保 ECG 片段长度一致\n",
    "    if len(ecg_values) < target_length:\n",
    "        ecg_values = list(ecg_values) + [0] * (target_length - len(ecg_values))\n",
    "\n",
    "    # 按血糖值分类\n",
    "    if glucose_value < 70:\n",
    "        hypo_list.append(ecg_values)\n",
    "    elif 70 <= glucose_value <= 130:\n",
    "        normal_list.append(ecg_values)\n",
    "    else:\n",
    "        hyper_list.append(ecg_values)\n",
    "\n",
    "# 保存到 CSV\n",
    "pd.DataFrame(hypo_list).to_csv(\"hypo.csv\", index=False, header=False, mode='a')\n",
    "pd.DataFrame(normal_list).to_csv(\"normal.csv\", index=False, header=False, mode='a')\n",
    "pd.DataFrame(hyper_list).to_csv(\"hyper.csv\", index=False, header=False, mode='a')\n",
    "\n",
    "print(\"数据划分完成，已保存到 hypo.csv, normal.csv, hyper.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "⚠️ 血糖测量时间 NaT 之前的 ECG 片段为空，跳过该测量点。\n",
      "✅ 数据划分完成：\n",
      "   - ecg_segments/healthy/001/hypo.csv 共追加 0 条数据\n",
      "   - ecg_segments/healthy/001/normal.csv 共追加 0 条数据\n",
      "   - ecg_segments/healthy/001/hyper.csv 共追加 0 条数据\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# 设置目标存储文件夹\n",
    "output_folder = \"ecg_segments/healthy/001\"\n",
    "os.makedirs(output_folder, exist_ok=True)  # 如果文件夹不存在，则创建\n",
    "\n",
    "# 读取 ECG 数据\n",
    "ecg_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_ecg_data/001.csv\")\n",
    "ecg_df[\"Time\"] = pd.to_datetime(ecg_df[\"Time\"], format=\"%d/%m/%Y %H:%M:%S.%f\", errors='coerce')  # 解析时间\n",
    "\n",
    "# 读取血糖数据\n",
    "glucose_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_glucose/001/glucose.csv\")\n",
    "glucose_df[\"datetime\"] = pd.to_datetime(glucose_df[\"date\"] + \" \" + glucose_df[\"time\"], format=\"%Y-%m-%d %H:%M:%S\", errors='coerce')\n",
    "\n",
    "# 获取 ECG 记录的最早和最晚时间\n",
    "ecg_start_time = ecg_df[\"Time\"].min()\n",
    "ecg_end_time = ecg_df[\"Time\"].max()\n",
    "\n",
    "# 目标 ECG 片段长度\n",
    "target_length = 5 * 60 * 250  # 5分钟，每秒250个采样点\n",
    "\n",
    "# 目标 CSV 文件路径\n",
    "hypo_file = os.path.join(output_folder, \"hypo.csv\")\n",
    "normal_file = os.path.join(output_folder, \"normal.csv\")\n",
    "hyper_file = os.path.join(output_folder, \"hyper.csv\")\n",
    "\n",
    "# 计数器（统计写入的数据条数）\n",
    "file_count = {\"hypo\": 0, \"normal\": 0, \"hyper\": 0}\n",
    "\n",
    "# 遍历血糖测量点\n",
    "for _, row in glucose_df.iterrows():\n",
    "    measure_time = row[\"datetime\"]  # 当前血糖测量时间\n",
    "    glucose_value = row[\"glucose\"]  # 当前血糖值\n",
    "\n",
    "    # **如果血糖测量时间超出 ECG 数据范围，直接跳过**\n",
    "    if measure_time < ecg_start_time:\n",
    "        continue  # 跳过早于 ECG 记录的时间点\n",
    "    if measure_time > ecg_end_time:\n",
    "        break  # **超出 ECG 记录的最晚时间，停止遍历**\n",
    "\n",
    "    # 选取前 5 分钟 ECG 片段\n",
    "    start_time = measure_time - pd.Timedelta(minutes=5)\n",
    "    ecg_segment = ecg_df[(ecg_df[\"Time\"] >= start_time) & (ecg_df[\"Time\"] < measure_time)]\n",
    "\n",
    "    # **如果 ECG 片段为空，打印提示信息并跳过**\n",
    "    if ecg_segment.empty:\n",
    "        print(f\"⚠️ 血糖测量时间 {measure_time} 之前的 ECG 片段为空，跳过该测量点。\")\n",
    "        continue\n",
    "\n",
    "    # 仅保留 EcgWaveform 数据\n",
    "    ecg_values = ecg_segment[\"EcgWaveform\"].values.flatten()\n",
    "\n",
    "    # 确保 ECG 片段长度一致\n",
    "    if len(ecg_values) < target_length:\n",
    "        ecg_values = list(ecg_values) + [0] * (target_length - len(ecg_values))\n",
    "\n",
    "    # 选择文件路径\n",
    "    if glucose_value <= 70:\n",
    "        output_file = hypo_file\n",
    "        file_key = \"hypo\"\n",
    "    elif 70 < glucose_value < 130:\n",
    "        output_file = normal_file\n",
    "        file_key = \"normal\"\n",
    "    else:\n",
    "        output_file = hyper_file\n",
    "        file_key = \"hyper\"\n",
    "\n",
    "    # **追加数据到 CSV，而不是覆盖**\n",
    "    file_exists = os.path.isfile(output_file)  # 检查文件是否已存在\n",
    "    pd.DataFrame([ecg_values]).to_csv(output_file, mode='a', index=False, header=not file_exists)\n",
    "\n",
    "    # 计数 +1\n",
    "    file_count[file_key] += 1\n",
    "\n",
    "# **最终打印写入的条数**\n",
    "print(f\"✅ 数据划分完成：\")\n",
    "print(f\"   - {hypo_file} 共追加 {file_count['hypo']} 条数据\")\n",
    "print(f\"   - {normal_file} 共追加 {file_count['normal']} 条数据\")\n",
    "print(f\"   - {hyper_file} 共追加 {file_count['hyper']} 条数据\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚠️ 血糖测量时间 2014-10-02 08:10:00 之前的 ECG 片段为空，跳过该测量点。\n",
      "✅ 数据划分完成：\n",
      "   - ecg_segments/healthy/017/hypo.csv 共追加 0 条数据\n",
      "   - ecg_segments/healthy/017/normal.csv 共追加 18 条数据\n",
      "   - ecg_segments/healthy/017/hyper.csv 共追加 4 条数据\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# 设置目标存储文件夹\n",
    "output_folder = \"ecg_segments/healthy/017\"\n",
    "os.makedirs(output_folder, exist_ok=True)  # 如果文件夹不存在，则创建\n",
    "\n",
    "# 读取 ECG 数据\n",
    "ecg_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_ecg_data/017.csv\")\n",
    "ecg_df[\"Time\"] = pd.to_datetime(ecg_df[\"Time\"], format=\"%d/%m/%Y %H:%M:%S.%f\", errors='coerce')  # 解析时间\n",
    "\n",
    "# 读取血糖数据\n",
    "glucose_df = pd.read_csv(\"ecg-glucose-data/healthy_subset_glucose/017/glucose.csv\")\n",
    "glucose_df[\"datetime\"] = pd.to_datetime(glucose_df[\"date\"] + \" \" + glucose_df[\"time\"], format=\"%Y-%m-%d %H:%M\", errors='coerce')\n",
    "\n",
    "# 获取 ECG 记录的最早和最晚时间\n",
    "ecg_start_time = ecg_df[\"Time\"].min()\n",
    "ecg_end_time = ecg_df[\"Time\"].max()\n",
    "\n",
    "# 目标 ECG 片段长度\n",
    "target_length = 5 * 60 * 250  # 5分钟，每秒250个采样点\n",
    "\n",
    "# 目标 CSV 文件路径\n",
    "hypo_file = os.path.join(output_folder, \"hypo.csv\")\n",
    "normal_file = os.path.join(output_folder, \"normal.csv\")\n",
    "hyper_file = os.path.join(output_folder, \"hyper.csv\")\n",
    "\n",
    "# 计数器（统计写入的数据条数）\n",
    "file_count = {\"hypo\": 0, \"normal\": 0, \"hyper\": 0}\n",
    "\n",
    "# 遍历血糖测量点\n",
    "for _, row in glucose_df.iterrows():\n",
    "    measure_time = row[\"datetime\"]  # 当前血糖测量时间\n",
    "    glucose_value = row[\"glucose\"]  # 当前血糖值\n",
    "\n",
    "    # **如果血糖测量时间超出 ECG 数据范围，直接跳过**\n",
    "    if measure_time < ecg_start_time:\n",
    "        continue  # 跳过早于 ECG 记录的时间点\n",
    "    if measure_time > ecg_end_time:\n",
    "        break  # **超出 ECG 记录的最晚时间，停止遍历**\n",
    "\n",
    "    # 选取前 5 分钟 ECG 片段\n",
    "    start_time = measure_time - pd.Timedelta(minutes=5)\n",
    "    ecg_segment = ecg_df[(ecg_df[\"Time\"] >= start_time) & (ecg_df[\"Time\"] < measure_time)]\n",
    "\n",
    "    # **如果 ECG 片段为空，打印提示信息并跳过**\n",
    "    if ecg_segment.empty:\n",
    "        print(f\"⚠️ 血糖测量时间 {measure_time} 之前的 ECG 片段为空，跳过该测量点。\")\n",
    "        continue\n",
    "\n",
    "    # 仅保留 EcgWaveform 数据\n",
    "    ecg_values = ecg_segment[\"EcgWaveform\"].values.flatten()\n",
    "\n",
    "    # 确保 ECG 片段长度一致\n",
    "    if len(ecg_values) < target_length:\n",
    "        ecg_values = list(ecg_values) + [0] * (target_length - len(ecg_values))\n",
    "\n",
    "    # 选择文件路径\n",
    "    if glucose_value <= 70:\n",
    "        output_file = hypo_file\n",
    "        file_key = \"hypo\"\n",
    "    elif 70 < glucose_value < 130:\n",
    "        output_file = normal_file\n",
    "        file_key = \"normal\"\n",
    "    else:\n",
    "        output_file = hyper_file\n",
    "        file_key = \"hyper\"\n",
    "\n",
    "    # **追加数据到 CSV，而不是覆盖**\n",
    "    file_exists = os.path.isfile(output_file)  # 检查文件是否已存在\n",
    "    pd.DataFrame([ecg_values]).to_csv(output_file, mode='a', index=False, header=not file_exists)\n",
    "\n",
    "    # 计数 +1\n",
    "    file_count[file_key] += 1\n",
    "\n",
    "# **最终打印写入的条数**\n",
    "print(f\"✅ 数据划分完成：\")\n",
    "print(f\"   - {hypo_file} 共追加 {file_count['hypo']} 条数据\")\n",
    "print(f\"   - {normal_file} 共追加 {file_count['normal']} 条数据\")\n",
    "print(f\"   - {hyper_file} 共追加 {file_count['hyper']} 条数据\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并完成！文件已保存到: /Users/wangyuxiang/Documents/exp/ecg-glucose/ecg_segments/normal.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 定义主文件夹路径\n",
    "main_folder = \"/Users/wangyuxiang/Documents/exp/ecg-glucose/ecg_segments\"  # 替换为你的主文件夹路径\n",
    "output_folder = os.path.join(main_folder, \"/Users/wangyuxiang/Documents/exp/ecg-glucose/ecg_segments\")  # 合并后的文件保存路径\n",
    "\n",
    "# 如果输出文件夹不存在，则创建\n",
    "if not os.path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "\n",
    "# 初始化一个空的 DataFrame 用于存储合并后的数据\n",
    "merged_data = pd.DataFrame()\n",
    "\n",
    "# 遍历主文件夹下的所有子文件夹\n",
    "for root, dirs, files in os.walk(main_folder):\n",
    "    for file in files:\n",
    "        if file == \"normal.csv\":  # 找到 hyper.csv 文件\n",
    "            file_path = os.path.join(root, file)  # 获取文件完整路径\n",
    "            # 读取 hyper.csv 文件\n",
    "            df = pd.read_csv(file_path)\n",
    "            # 将数据追加到 merged_data 中\n",
    "            merged_data = pd.concat([merged_data, df], ignore_index=True)\n",
    "\n",
    "# 将合并后的数据保存到新的 hyper.csv 文件中\n",
    "output_file_path = os.path.join(output_folder, \"normal.csv\")\n",
    "merged_data.to_csv(output_file_path, index=False, header=False)\n",
    "\n",
    "print(f\"合并完成！文件已保存到: {output_file_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.signal import find_peaks\n",
    "\n",
    "def compute_hr_stats_for_row(ecg_waveform, fs=250):\n",
    "    if len(ecg_waveform) < fs * 2:\n",
    "        return pd.DataFrame(columns=['SDNN', 'RMSSD', 'nn_50', 'pNN_50', 'meanHR', 'SDHR', 'meanRR', 'TINN', 'HRVTriIndex'])\n",
    "\n",
    "    # **1. R 波峰检测**\n",
    "    peaks, _ = find_peaks(ecg_waveform, prominence=0.2, distance=fs * 0.3)  # 降低 prominence，提高检测率\n",
    "\n",
    "    if len(peaks) < 2:\n",
    "        print(\"⚠️ R 波检测失败，ECG 可能有问题\")\n",
    "        return pd.DataFrame(columns=['SDNN', 'RMSSD', 'nn_50', 'pNN_50', 'meanHR', 'SDHR', 'meanRR', 'TINN', 'HRVTriIndex'])\n",
    "\n",
    "    # **2. 计算 RR 间隔 & HR**\n",
    "    rr_ints = np.diff(peaks) / fs  # RR 间隔（秒）\n",
    "    hr = 60 / rr_ints if len(rr_ints) > 0 else np.nan  # 心率（bpm）\n",
    "\n",
    "    # **3. 计算 HRV 指标**\n",
    "    results = pd.DataFrame({\n",
    "        'SDNN': np.std(rr_ints) if len(rr_ints) > 1 else np.nan,\n",
    "        'RMSSD': np.sqrt(np.mean(np.square(np.diff(rr_ints)))) if len(rr_ints) > 1 else np.nan,\n",
    "        'nn_50': np.sum(np.abs(np.diff(rr_ints)) > 0.05) if len(rr_ints) > 1 else 0,\n",
    "        'pNN_50': (np.sum(np.abs(np.diff(rr_ints)) > 0.05) / len(rr_ints)) if len(rr_ints) > 1 else np.nan,\n",
    "        'meanHR': np.mean(hr) if len(hr) > 0 else np.nan,\n",
    "        'SDHR': np.std(hr) if len(hr) > 1 else np.nan,\n",
    "        'meanRR': np.mean(rr_ints) if len(rr_ints) > 0 else np.nan,\n",
    "        'TINN': np.max(rr_ints) - np.min(rr_ints) if len(rr_ints) > 1 else np.nan,\n",
    "        'HRVTriIndex': np.sum(np.abs(np.diff(rr_ints))) / (np.sum(rr_ints) + 1e-6)  # 避免除零\n",
    "    }, index=[0])\n",
    "\n",
    "    return results\n",
    "\n",
    "def compute_hr_stats_for_df(df):\n",
    "    hrv_results = []\n",
    "    for i, row in df.iterrows():\n",
    "        ecg_waveform = row.values.astype(float)  # 确保是浮点数\n",
    "        hrv_result = compute_hr_stats_for_row(ecg_waveform)\n",
    "        if not hrv_result.empty:\n",
    "            hrv_results.append(hrv_result.iloc[0].values)\n",
    "\n",
    "    return pd.DataFrame(hrv_results, columns=['SDNN', 'RMSSD', 'nn_50', 'pNN_50', 'meanHR', 'SDHR', 'meanRR', 'TINN', 'HRVTriIndex']) \n",
    "\n",
    "\n",
    "df = pd.read_csv(\"ecg_segments/normal.csv\", header=None)  # 读取 ECG 数据（假设每行是一个片段）\n",
    "hrv_df = compute_hr_stats_for_df(df)  # 计算 HRV\n",
    "hrv_df.to_csv(\"ecg_segments/hrv_normal.csv\", index=False)  # 保存结果\n",
    "print(\"HRV 计算完成，结果已保存\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 降采样完成，原始形状: (336, 75000)，新形状: (336, 147)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 读取数据\n",
    "hyper_data_frame = pd.read_csv(\"ecg_segments/filter/seg/filter_hypo.csv\", header=None)\n",
    "\n",
    "# 设定间隔采样率\n",
    "sampling_factor = 512  # 例如，每 2 个点取 1 个（降低一半采样率）\n",
    "\n",
    "# 进行降采样\n",
    "hyper_downsampled = hyper_data_frame.iloc[:, ::sampling_factor]\n",
    "\n",
    "# 保存降采样后的数据\n",
    "hyper_downsampled.to_csv(\"ecg_segments/filter/512seg/hypo_512.csv\", index=False, header=False)\n",
    "\n",
    "print(f\"✅ 降采样完成，原始形状: {hyper_data_frame.shape}，新形状: {hyper_downsampled.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "❌ 第 1440 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1441 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1880 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1881 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1920 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1990 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1991 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1993 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1994 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 1995 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 2098 行 ECG 被剔除（无效 HRV 数据）\n",
      "❌ 第 2122 行 ECG 被剔除（无效 HRV 数据）\n",
      "✅ 处理完成：12 行 ECG 被剔除，结果已保存到 ecg_segments/filter/filter_hyper.csv\n",
      "✅ HRV 数据已保存到 ecg_segments/filter/hrv/hrv_hyper.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.signal import find_peaks\n",
    "import os\n",
    "\n",
    "def compute_hr_stats_for_row(ecg_waveform, fs=250):\n",
    "    \"\"\" 计算单行 ECG 数据的 HRV 指标 \"\"\"\n",
    "    if len(ecg_waveform) < fs * 2:\n",
    "        return None  # 数据长度不足，无法计算\n",
    "\n",
    "    # **R 波峰检测**\n",
    "    peaks, _ = find_peaks(ecg_waveform, prominence=0.2, distance=fs * 0.3)\n",
    "    \n",
    "    if len(peaks) < 2:\n",
    "        return None  # RR 间隔不足，无法计算\n",
    "\n",
    "    # **计算 RR 间隔 & HR**\n",
    "    rr_ints = np.diff(peaks) / fs\n",
    "    if len(rr_ints) < 2:\n",
    "        return None  # RR 间隔太少，无法计算\n",
    "\n",
    "    hr = 60 / rr_ints\n",
    "\n",
    "    # **计算 HRV 指标**\n",
    "    results = {\n",
    "        'SDNN': np.std(rr_ints),\n",
    "        'RMSSD': np.sqrt(np.mean(np.square(np.diff(rr_ints)))),\n",
    "        'nn_50': np.sum(np.abs(np.diff(rr_ints)) > 0.05),\n",
    "        'pNN_50': np.sum(np.abs(np.diff(rr_ints)) > 0.05) / len(rr_ints),\n",
    "        'meanHR': np.mean(hr),\n",
    "        'SDHR': np.std(hr),\n",
    "        'meanRR': np.mean(rr_ints),\n",
    "        'TINN': np.max(rr_ints) - np.min(rr_ints) if len(rr_ints) > 1 else np.nan,\n",
    "        'HRVTriIndex': np.sum(np.abs(np.diff(rr_ints))) / (np.sum(rr_ints) + 1e-6)  # 避免除零\n",
    "    }\n",
    "\n",
    "    # **如果 HRV 结果包含 NaN，返回 None**\n",
    "    if any(np.isnan(v) for v in results.values()):\n",
    "        return None\n",
    "\n",
    "    return results\n",
    "\n",
    "def process_ecg_file(input_file, output_ecg_file, output_hrv_file):\n",
    "    \"\"\" 读取 ECG 数据，剔除无效行，并保存处理后的 ECG 和 HRV 指标 \"\"\"\n",
    "    df = pd.read_csv(input_file, header=None)  # 读取 ECG 原始数据\n",
    "\n",
    "    valid_ecg = []\n",
    "    hrv_data = []\n",
    "    removed_count = 0\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        ecg_waveform = row.values.astype(float)  # 确保是浮点数\n",
    "        hrv_result = compute_hr_stats_for_row(ecg_waveform)\n",
    "\n",
    "        if hrv_result is not None:\n",
    "            valid_ecg.append(row.values)  # HRV 计算成功，保留 ECG\n",
    "            hrv_data.append(hrv_result)  # 记录 HRV 指标\n",
    "        else:\n",
    "            removed_count += 1\n",
    "            print(f\"❌ 第 {i + 1} 行 ECG 被剔除（无效 HRV 数据）\")\n",
    "\n",
    "    # **保存剔除后的 ECG 数据**\n",
    "    if valid_ecg:\n",
    "        new_df = pd.DataFrame(valid_ecg)\n",
    "        new_df.to_csv(output_ecg_file, index=False, header=False)\n",
    "        print(f\"✅ 处理完成：{removed_count} 行 ECG 被剔除，结果已保存到 {output_ecg_file}\")\n",
    "    else:\n",
    "        print(\"⚠️ 所有 ECG 数据都无效，未生成 ECG 新文件\")\n",
    "\n",
    "    # **保存计算出的 HRV 数据**\n",
    "    if hrv_data:\n",
    "        hrv_df = pd.DataFrame(hrv_data)\n",
    "        hrv_df.to_csv(output_hrv_file, index=False)\n",
    "        print(f\"✅ HRV 数据已保存到 {output_hrv_file}\")\n",
    "    else:\n",
    "        print(\"⚠️ 所有 ECG 数据都无效，未生成 HRV 新文件\")\n",
    "\n",
    "# **创建输出目录**\n",
    "output_dir = \"ecg_segments\"\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# **示例使用**\n",
    "input_ecg_file = \"ecg_segments/hyper.csv\"  # 原始 ECG 文件\n",
    "output_ecg_file = os.path.join(output_dir, \"filter/filter_hyper.csv\")  # 处理后的 ECG 文件\n",
    "output_hrv_file = os.path.join(output_dir, \"filter/hrv/hrv_hyper.csv\")  # 计算出的 HRV 指标文件\n",
    "\n",
    "process_ecg_file(input_ecg_file, output_ecg_file, output_hrv_file)"
   ]
  }
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